Application of MALDI-TOF MS and machine learning for the detection of SARS-CoV-2 and non-SARS-CoV-2 respiratory infections

Yegorov S. Kadyrova I. Korshukov I. Sultanbekova A. Kolesnikova Y. Barkhanskaya V. Bashirova T. Zhunusov Y. Li Y. Parakhina V. Kolesnichenko S. Baiken Y. Matkarimov B. Vazenmiller D. Miller M.S. Hortelano G.H. Turmukhambetova A. Chesca A.E. Babenko D.
May 2024 American Society for Microbiology

Microbiology Spectrum
2024 #12 Issue 5

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) could aid the diagnosis of acute respiratory infections (ARIs) owing to its affordability and high-throughput capacity. MALDI-TOF MS has been proposed for use on commonly available respiratory samples, without specialized sample preparation, making this technology especially attractive for implementation in low-resource regions. Here, we assessed the utility of MALDI-TOF MS in differentiating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vs non-COVID acute respiratory infections (NCARIs) in a clinical lab setting in Kazakhstan. Nasopharyngeal swabs were collected from inpatients and outpatients with respiratory symptoms and from asymptomatic controls (ACs) in 2020–2022. PCR was used to differentiate SARS-CoV-2+ and NCARI cases. MALDI-TOF MS spectra were obtained for a total of 252 samples (115 SARS-CoV-2+, 98 NCARIs, and 39 ACs) without specialized sample preparation. In our first sub-analysis, we followed a published protocol for peak preprocessing and machine learning (ML), trained on publicly available spectra from South American SARS-CoV-2+ and NCARI samples. In our second sub-analysis, we trained ML models on a peak intensity matrix representative of both South American (SA) and Kazakhstan (Kaz) samples. Applying the established MALDI-TOF MS pipeline “as is” resulted in a high detection rate for SARS-CoV-2+ samples (91.0%), but low accuracy for NCARIs (48.0%) and ACs (67.0%) by the top-performing random forest model. After re-training of the ML algorithms on the SA-Kaz peak intensity matrix, the accuracy of detection by the top-performing support vector machine with radial basis function kernel model was at 88.0%, 95.0%, and 78% for the Kazakhstan SARS-CoV-2+, NCARI, and AC subjects, respectively, with a SARS-CoV-2 vs rest receiver operating characteristic area under the curve of 0.983 [0.958, 0.987]; a high differentiation accuracy was maintained for the South American SARS-CoV-2 and NCARIs. MALDI-TOF MS/ML is a feasible approach for the differentiation of ARI without specialized sample preparation. The implementation of MALDI-TOF MS/ML in a real clinical lab setting will necessitate continuous optimization to keep up with the rapidly evolving landscape of ARI.

acute respiratory infection , COVID-19 , machine learning , MALDI-TOF MS , SARS-CoV-2

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Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster Immunology Research Centre, McMaster University, Hamilton, ON, Canada
School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan
Research Centre, Karaganda Medical University, Karaganda, Kazakhstan
City Centre for Primary Medical and Sanitary Care, Karaganda, Kazakhstan
Infectious Disease Centre of the Karaganda Regional Clinical Hospital, Karaganda, Kazakhstan
Department of Internal Diseases, Karaganda Medical University, Karaganda, Kazakhstan
National Laboratory Astana, Centre for Life Sciences, Nazarbayev University, Astana, Kazakhstan
School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan
Faculty of Medicine, Transilvania University, Brașov, Romania

Department of Biochemistry and Biomedical Sciences
School of Sciences and Humanities
Research Centre
City Centre for Primary Medical and Sanitary Care
Infectious Disease Centre of the Karaganda Regional Clinical Hospital
Department of Internal Diseases
National Laboratory Astana
School of Engineering and Digital Sciences
Faculty of Medicine

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